ArticleComputational psychiatry (Cambridge, Mass.)2023
Reliability of Decision-Making and Reinforcement Learning Computational Parameters.
Article in Computational psychiatry (Cambridge, Mass.), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
20 citing papers in PubMed.
- The recoverability, reliability, and generalizability of reward processing parameters and relation to mental health symptoms.Psychological medicine · 2026Article
- Probing biomarkers and clinical utility of reward learning across species using the Probabilistic Reward Task: 20 years of findings.Nature. Mental health · 2026Article
- Anhedonic Traits Do Not Impair Performance in a 3-Arm Bandit Task.Computational psychiatry (Cambridge, Mass.) · 2026Article
- Emotion recognition from multimodal biosignals: supervised and unsupervised machine learning approaches based on EEG and GSR.Frontiers in psychology · 2026Article
- Large-scale experimental investigation of the reliability of confidence measures.Communications psychology · 2025Article
- A computational approach to understanding effort-based decision-making in depression.Psychological medicine · 2025Article
- A computational approach to understanding effort-based decision-making in depression.bioRxiv : the preprint server for biology · 2025Article
- A common alteration in effort-based decision-making in apathy, anhedonia, and late circadian rhythm.eLife · 2025Article
- Does the reliability of computational models truly improve with hierarchical modeling? Some recommendations and considerations for the assessment of model parameter reliability : Reliability of computational model parameters.Psychonomic bulletin & review · 2024Review
- Consistency within change: Evaluating the psychometric properties of a widely used predictive-inference task.Behavior research methods · 2024Article
- Estimating the Reliability and Stability of Cognitive Processes Contributing to Responses on the Implicit Association Test.Personality & social psychology bulletin · 2024Article
- Dynamic computational phenotyping of human cognition.Nature human behaviour · 2024Article
- Timing along the cardiac cycle modulates neural signals of reward-based learning.Nature communications · 2024Article
- Active reinforcement learning versus action bias and hysteresis: control with a mixture of experts and nonexperts.PLoS computational biology · 2024Article
- Test-Retest Reliability of Two Computationally-Characterised Affective Bias Tasks.Computational psychiatry (Cambridge, Mass.) · 2024Article
- Test-retest reliability of behavioral and computational measures of advice taking under volatility.PloS one · 2024Article
- Reliability of gamified reinforcement learning in densely sampled longitudinal assessments.PLOS digital health · 2023Article
- Identifying Transdiagnostic Mechanisms in Mental Health Using Computational Factor Modeling.Biological psychiatry · 2023Review
- Self-judgment dissected: A computational modeling analysis of self-referential processing and its relationship to trait mindfulness facets and depression symptoms.Cognitive, affective & behavioral neuroscience · 2023Article
- Test-retest reliability of computational parameters versus manifest behavior for decisional flexibility in psychosis.Psychological assessmentArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
Computational models can offer mechanistic insight into cognition and therefore have the potential to transform our understanding of psychiatric disorders and their treatment. For translational efforts to be successful, it is imperative that computational measures capture individual characteristics reliably. Here we examine the reliability of reinforcement learning and economic models derived from two commonly used tasks. Healthy individuals (N = 50) completed a restless four-armed bandit and a calibrated gambling task twice, two weeks apart. Reward and punishment learning rates from the reinforcement learning model showed good reliability and reward and punishment sensitivity from the same model had fair reliability; while risk aversion and loss aversion parameters from a prospect theory model exhibited good and excellent reliability, respectively. Both models were further able to predict future behaviour above chance within individuals. This prediction was better when based on participants' own model parameters than other participants' parameter estimates. These results suggest that reinforcement learning, and particularly prospect theory parameters, as derived from a restless four-armed bandit and a calibrated gambling task, can be measured reliably to assess learning and decision-making mechanisms. Overall, these findings indicate the translational potential of clinically-relevant computational parameters for precision psychiatry.
Indexed as
Identifiers
What Socratic holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.